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\section{Introduction}
In this report, we examine techniques to use support vector machines
(SVMs) to build a model binary target features. To illustrate these
techniques, we will apply them in building a model to accurately predict
whether a direct mailing will result in a donation using the KDD Cup 1998 Data
\cite{kdd_cup_data}.  Evaluating techniques are
important because they demonstrate how to improve the accuracy of the model
which results in better estimates to guide decisions for the future. 

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However, like most real world data, we notice that the number of negative
examples dwarfs the number of positive examples that we want to identify.
Consequently, we do not want to train an SVM with just high accuracy because
it will trivially just choose that everything is a negative example and result
in a high accuracy rate. The interesting feature in this data set is the rare
positive examples, so we target a model with a high accuracy and recall rate for the
positive examples to correctly identify as many of these examples. 

The remainder of this report is organized as follows. Section \ref{sec:flow}
explains the optimal flow we found to preprocess the data and create a model. Section
\ref{sec:methodology} explains the techniques used to prune and preprocess
data. Section \ref{sec:results} presents our results from these techniques
and our evaluation. Section \ref{sec:conclusion} concludes. 
